New Approach Evaluating Peatland Fires in Indonesian Factors

Abstract

Until 2018, the El Niño–Southern Oscillation (ENSO) was used as an explanation for fires in Indonesia’s peatlands. However, when the 2019 fires occurred independently of El Niño, more suitable indicators and methods were required to (a) analyze, (b) evaluate and (c) forecast peatland fires. In this study, we introduced the OLR–MC index—one of the rain-related indices derived from OLR (outgoing longwave radiation) in MC (maritime continent) area in Indonesia. This index showed stronger correlation with active peatland fires than the conventional ENSO index, and is likely to be able to respond to heat and dry weather supposed to be under climate-change conditions. We then analyzed peatland fires in the top six fire years from 2002 to 2018 and showed that peatland fires occurred in three stages—surface fire, shallow peatland fire and deep peatland fire. To explain each stage, we proposed a one-dimensional groundwater level (GWL) prediction model (named as MODEL-0). MODEL-0 predicts GWL from daily rainfall. Analysis using MODEL-0 showed the GWL thresholds for the three fire stages were between -300 mm and -500 mm; peatland fire activities during the three fire stages were dependent on these GWL values. The validity of MODEL-0 was shown by comparison with the measured values of GWL in the top three fire years.

Classification of Active Fires and Weather Conditions in the Lower Amur River Basin

“…The daily HSs were used to identify spatial distribution and temporal occurrence in the SKK. A period of high fire activity was defined here as consecutive fire days when the number of daily hotspots was greater than 300 [16,17,27]. Several active fire-periods from 2003 to 2019 were identified and ranked by the total number of HSs of each active fire-period.…”

Analysis of Surface Concentration of Black Carbon in Riau Province During the 2019 Forest Fires Using Merra-2 Reanalysis Data

“…During those periods, many regions in Indonesia receive less amount of rainfall [20] wherein this condition threatens to increase forest fire incidents [21,22]. Figure 1a [23,24]. Many previous studies revealed that El Nino events tend to decrease rainfall over Indonesia Maritime Continent (IMC) [25], hence prolonging the dry season which then leads to severe forest fires.…”

Hasil Studi Pola Kebakaran Lahan Gambut melalui Citra Satelit Sentinel-2 dengan Pengimplementasian Machine Learning Metode Random Forest : Kajian Literatur

“…Namun sewaktu tahun 2019 timbul kebakaran yang independen terhadap El Niño, dibutuhkan indikator dan metode yang lebih mumpuni untuk (a) menganalisis, (b) mengevaluasi dan (c) memprediksi kebakaran lahan gambut. Hayasaka, et al, (2020) menganalisis kebakaran lahan gambut terbesar dalam rentang waktu 6 tahun dari 2002 hingga 2018 dan menemukan bahwa kebakaran lahan gambut terjadi dalam tiga tahapan-kebakaran permukaan, kebakaran gambut dangkal, dan kebakaran gambut dalam. Untuk menjelaskan setiap tahapan, diusulkan model prediksi Ground Water Level (GWL) satu-dimensi dengan nama MODEL-0.…”